Abstract: Disclosed subject matter relates to Peripheral Blood Smear (PBS) that determines an area to be scanned in PBS for analysis. A PBS analysing system captures a focused image at each of plurality of positions in the PBS and determines Quality Indicators (QIs) in focused image. Further, a region is identified in PBS where QIs of focused image satisfy predefined QI threshold limits, as a monolayer region of PBS and determines an initiation point in monolayer region based on cell count value and co-ordinates of each of the plurality of positions located in the monolayer region. Finally, the area to be scanned in monolayer region is determined based on the initiation point and a predefined scan pattern. Determining the area to be scanned yields accurate and faster results.

Abstract: The present disclosure relates to a method and system for determining Total Count (TC) of RBCs in a Peripheral Blood Smear (PBS). The system receives a plurality of images from the monolayer of the PBS. Further, the system extracts, segments and identifies RBCs in each of the plurality of images using deep learning models. The system computes a value of each variable of a set of variables for each of the plurality of images. The set of variables includes foreground non-pallor area, density of RBCs, cell count, cell count ratio, foreground area and foreground hole-filled area. Furthermore, the system computes statistical parameters for each variable, over the plurality of images. The statistical parameters are provided as an input to supervised learning model, to determine TC of RBCs. Thus, the TC estimation system provides an efficient and robust method for estimating TC of RBCs using plurality of images of the PBS.

Abstract: The present disclosure relates to a method and system for determining Total Count (TC) of RBCs in a Peripheral Blood Smear (PBS). The system receives a plurality of images from the monolayer of the PBS. Further, the system extracts, segments and identifies RBCs in each of the plurality of images using deep learning models. The system computes a value of each variable of a set of variables for each of the plurality of images. The set of variables includes foreground non-pallor area, density of RBCs, cell count, cell count ratio, foreground area and foreground hole-filled area. Furthermore, the system computes statistical parameters for each variable, over the plurality of images. The statistical parameters are provided as an input to supervised learning model, to determine TC of RBCs. Thus, the TC estimation system provides an efficient and robust method for estimating TC of RBCs using plurality of images of the PBS.

Abstract: Disclosed subject matter relates to Peripheral Blood Smear (PBS) that determines an area to be scanned in PBS for analysis. A PBS analysing system captures a focused image at each of plurality of positions in the PBS and determines Quality Indicators (QIs) in focused image. Further, a region is identified in PBS where QIs of focused image satisfy predefined QI threshold limits, as a monolayer region of PBS and determines an initiation point in monolayer region based on cell count value and co-ordinates of each of the plurality of positions located in the monolayer region. Finally, the area to be scanned in monolayer region is determined based on the initiation point and a predefined scan pattern. Determining the area to be scanned yields accurate and faster results.